Biology subjects

Unterman, I.

Publications and source records attributed to Unterman, I..

6 recordsLinked to original sources

bioRxiv2026

Improved deconvolution of circulating tumor DNA from ultra-low-pass whole-genome methylation sequencing using CelFiE-ISH

Liquid biopsy using ultra-low-pass whole-genome sequencing (ULP-WGS, [~]0.25x coverage) is a promising tool to detect circulating tumor DNA (ctDNA) for cancer management, and the use of the native Oxford Nanopore (ONT) sequencing platform adds DNA methylation to the set of detectable features. Here, we test the performance of methylation-based cell-type deconvolution in ULP-WGS samples from diverse epithelial malignancies and investigate several new computational strategies using our CelFiE-ISH deconvolution framework. We find that incorporating larger numbers of markers restricted to the epithelial cell lineage can reduce the cancer fraction limit of detection down to 1.7-3.1%, matching or exceeding the 3% floor of established copy-number alteration (CNA) benchmarks. Our study provides a useful strategy for analysis of ULP-WGS ONT data and indicates that marker selection remains a key challenge for analyzing methylation-based cancer datasets.

genomics↗

bioRxiv2023

Multi-cell type deconvolution using a probabilistic model for single-molecule DNA methylation haplotypes

BackgroundDeconvolution is used to estimate the proportion of mixed cell types from tissue or blood samples based on genomic profiling. DNA methylation is commonly used because specific CpG positions reflect cell type identity and can be accurately measured at either the population or single-molecule level. Methylation sequencing techniques can profile multiple individual CpGs on a single DNA molecule, but few deconvolution models have been developed to exploit these single-molecule methylation haplotypes for cell type deconvolution. Results and ConclusionsWe used simulated whole-genome methylation data and in silico mixtures of real data to compare existing deconvolution tools with two new models developed here. We found that adapting an existing model CelFiE to incorporate methylation haplotype information improved deconvolution accuracy by [~]30% over other tools, including the original CelFiE. In addition to overall higher accuracy, our new tool CelFiE Integrated Single-molecule Haplotypes (or CelFiE-ISH) outperformed others in detecting rare cell types present at 0.1% and below. Detection of rare cell types is important for the analysis of circulating DNA, which we demonstrate using a patient-derived plasma sequencing dataset.Finally,we show that marker selection strategy has a strong effect on deconvolution accuracy, concluding that haplotype-aware deconvolution can take advantage of markers tailored for that purpose.

bioinformatics↗

bioRxiv2022

A local sequence signature defines a subset of heterochromatin-associated CpGs with minimal loss of methylation in healthy tissues but extensive loss in cancer

Global loss of DNA methylation in mammalian genomes occurs during aging and cancer, primarily in heterochromatin-associated Partially Methylated Domains (PMDs). It has previously been shown that local sequence context (100bp) has a strong influence on the rate of demethylation of individual CpG dinucleotides within PMDs. Here, we train a deep learning model to capture this sequence dependence, finding that methylation loss in healthy tissues and cancer can be predicted with high accuracy based on the 150bp surrounding a CpG. We use a published whole-genome map of the re-methylation rate of newly synthesized DNA during mitosis to show that CpGs with a "slow-loss" sequence context are efficiently re-methylated, while CpGs with a "fast-loss" sequence context are inefficiently re-methylated. Intriguingly, we find that the 10% most slow-loss CpGs lose almost no DNA methylation in healthy cell types, but lose significant DNA methylation in many cancer types. This finding suggests that loss of DNA methylation at slow-loss CpGs could underlie some cancer-specific transcriptional deregulation that has been linked to DNA hypomethylation, including the derepression of cancer antigens and transposable elements.

genomics↗

bioRxiv2021

Cross-species identification of cancer-resistance associated genes uncovers their relevance to human cancer risk

Cancer is an evolutionarily conserved disease that occurs in a wide variety of species. We applied a comparative genomics approach to systematically characterize the genes whose conservation levels significantly correlates positively (PC) or negatively (NC) with a broad spectrum of cancer-resistance estimates, computed across almost 200 vertebrate species. PC genes are enriched in pathways relevant to tumor suppression including cell cycle, DNA repair, and immune response, while NC genes are enriched with a host of metabolic pathways. The conservation levels of the PC and NC genes in a species serve to build the first genomics-based predictor of its cancer resistance score. We find that PC genes are less tolerant to loss of function (LoF) mutations, are enriched in cancer driver genes and are associated with germline mutations that increase human cancer risk. Furthermore, their expression levels are associated with lifetime cancer risk across human tissues. Finally, their knockout in mice results in increased cancer incidence. In sum, we find that many genes associated with cancer resistance across species are implicated in human cancers, pointing to several additional candidate genes that may have a functional role in human cancer.

cancer biology↗

bioRxiv2021

Expanding the MECP2 network using comparative genomics reveals potential therapeutic targets for Rett syndrome

Inactivating mutations in the Methyl-CpG Binding Protein 2 (MECP2) gene are the main cause of Rett syndrome (RTT). Despite extensive research into MECP2 function, no treatments for RTT are currently available. Here we use an evolutionary genomics approach to construct an unbiased MECP2 gene network, using 1,028 eukaryotic genomes to prioritize proteins with strong co-evolutionary signatures with MECP2. Focusing on proteins targeted by FDA approved drugs led to three promising candidates, two of which were previously linked to MECP2 function (IRAK, KEAP1) and one that was not (EPOR). We show that each of these compounds has the ability to rescue different phenotypes of MECP2 inactivation in cultured human neural cell types, and appear to act on Nuclear Factor Kappa B (NF-{kappa}B) signaling in inflammation. This study highlights the potential of comparative genomics to accelerate drug discovery, and yields potential new avenues for the treatment of RTT. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=113 SRC="FIGDIR/small/431162v1_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@1d7e0f8org.highwire.dtl.DTLVardef@1f2c602org.highwire.dtl.DTLVardef@1a5dd7dorg.highwire.dtl.DTLVardef@7071c8_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2020

Conservation motifs - a novel evolutionary-based classification of proteins

Cross-species protein conservation patterns, as directed by natural selection, are indicative of the interplay between protein function, protein-protein interaction and evolution. Since the beginning of the genomic era, proteins were characterized as either conserved or not conserved. This simple classification became archaic and cursory once data on protein orthologs became available for thousands of species. To enrich the language used to describe protein conservation patterns, and to understand their biological significance, we classified 20,294 human proteins against 1096 species. Analyses of the conservation patterns of human proteins in different eukaryotic clades yielded extremely variable and rich patterns that had never been characterized or studied before. Using mathematical classifications, we defined seven conservation motifs: Steps, Critical, Lately Developed, Plateau, Clade Loss, Trait Loss and Gain, which describe the evolution of human proteins. Overall, our work offers novel terms for conservation patterns and defines a new language intended to comprehensively describe protein evolution. This novel terminology enables the classification of proteins based on evolution, reveals aspects of protein evolution, and improves the understanding of protein functions.

evolutionary biology↗